Developments in 3D Echocardiography

2009 ◽  
Vol 5 (2) ◽  
pp. 10 ◽  
Author(s):  
Jose Luis Zamorano ◽  
Keyword(s):  
3D Echocardiography ◽  
Resonance Imaging ◽  
Clinical Tool ◽  
3D Images ◽  
Processing Power ◽  
2D Echocardiography ◽  
Automated Quantification ◽  
Heart Cycle ◽  
Functional Mechanisms ◽  
Full Volume

3D echocardiography (3DE) will gain increasing acceptance as a routine clinical tool as the technology evolves due to advances in technology and computer processing power. Images obtained from 3DE provide more accurate assessment of complex cardiac anatomy and sophisticated functional mechanisms compared with conventional 2D echocardiography (2DE), and are comparable to those achieved with magnetic resonance imaging. Many of the limitations associated with the early iterations of 3DE prevented their widespread clinical application. However, recent significant improvements in transducer and post-processing software technologies have addressed many of these issues. Furthermore, the most recent advances in the ability to image the entire heart in realtime and fully automated quantification have poised 3DE to become more ubiquitous in clinical routine. Realtime 3DE (RT3DE) systems offer further improvements in the diagnostic and treatment planning capabilities of cardiac ultrasound. Innovations such as the ability to acquire non-stitched, realtime, full-volume 3D images of the heart in a single heart cycle promise to overcome some of the current limitations of current RT3DE systems, which acquire images over four to seven cardiac cycles, with the need for gating and the potential for stitch artefacts.

scholarly journals Deep learning based MRI contrast synthesis using full volume prediction

2021 ◽  
Author(s):  
Jose V Manjon ◽  
Jose E Romero ◽  
Pierrick Coupé
Keyword(s):  
Three Dimensional ◽  
Patient Comfort ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Complementary Information ◽  
Mri Contrast ◽  
Ambiguity Reduction ◽  
Magnetic Resonance Imaging Mri ◽  
Time Limitations ◽  
Full Volume

Abstract In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

scholarly journals Magnetic resonance imaging in the diagnosis of white matter signal abnormalities

2018 ◽  
Vol 31 (4) ◽  
pp. 362-371 ◽  
Author(s):  
Ravi Datar ◽  
Asuri Narayan Prasad ◽  
Keng Yeow Tay ◽  
Charles Anthony Rupar ◽  
Pavlo Ohorodnyk ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
Tertiary Care ◽  
Diagnostic Utility ◽  
Care Hospital ◽  
Resonance Imaging ◽  
Clinical Tool ◽  
Diagnostic Challenge ◽  
Mri Scans

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.

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